期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 29, 期 1, 页码 30-43出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2009.2021941
关键词
AdaBoost; Alzheimer's disease; hippocampal segmentation; support vector machines (SVMs); surface modeling
类别
资金
- UCLA Center for Computational Biology under National Institute of Health [U54 RR021813]
- National Institute for Biomedical Imaging and Bioengineering
- National Center for Research Resources
- National Institute on Aging
- National Library of Medicine
- National Institute for Child Health and Development [EB01651, RR019771, HD050735, AG016570, LM05639]
- NIA [K23 AG026803, P50 AG16570]
- EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH &HUMAN DEVELOPMENT [R01HD050735] Funding Source: NIH RePORTER
- NATIONAL CENTER FOR RESEARCH RESOURCES [R21RR019771, U54RR021813] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON AGING [P50AG016570, K23AG026803] Funding Source: NIH RePORTER
- NATIONAL LIBRARY OF MEDICINE [R01LM005639] Funding Source: NIH RePORTER
We compared four automated methods for hippocampal segmentation using different machine learning algorithms: 1) hierarchical AdaBoost, 2) support vector machines (SVM) with manual feature selection, 3) hierarchical SVM with automated feature selection (Ada-SVM), and 4) a publicly available brain segmentation package (FreeSurfer). We trained our approaches using T1-weighted brain MRIs from 30 subjects [10 normal elderly, 10 mild cognitive impairment (MCI), and 10 Alzheimer's disease (AD)], and tested on an independent set of 40 subjects (20 normal, 20 AD). Manually segmented gold standard hippocampal tracings were available for all subjects (training and testing). We assessed each approach's accuracy relative to manual segmentations, and its power to map AD effects. We then converted the segmentations into parametric surfaces to map disease effects on anatomy. After surface reconstruction, we computed significance maps, and overall corrected p-values, for the 3-D profile of shape differences between AD and normal subjects. Our AdaBoost and Ada-SVM segmentations compared favorably with the manual segmentations and detected disease effects as well as FreeSurfer on the data tested. Cumulative p-value plots, in conjunction with the false discovery rate method, were used to examine the power of each method to detect correlations with diagnosis and cognitive scores. We also evaluated how segmentation accuracy depended on the size of the training set, providing practical information for future users of this technique.
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